The grass is cut regularly by our mowers and grazed on by cows and sheep, yet continues to grow back.  The secret to its remarkable regenerative powers lies in part in the shape of its leaves, but how that shape arises has been a topic of longstanding debate. 

The debate is relevant to our staple crops wheat, rice, and maize because they are members of the grass family with the same type of leaf.

The mystery of grass leaf formation has now been unraveled by a John Innes Centre team using the latest computational modeling and developmental genetic techniques. 

The John Innes Centre (JIC), located in Norwich, Norfolk, England, is an independent center for research and training in plant and microbial science founded in 1910.

One of the corresponding authors Professor Enrico Coen said of the findings which appear in Science: “The grass leaf has been a conundrum.  By formulating and testing different models for its evolution and development we’ve shown that current theories are likely incorrect and that a discarded idea proposed the 19^th century is much nearer the mark.”

Flowering plants can be categorized into monocots and eudicots. Monocots, which include the grass family, have leaves that encircle the stem at their base and have parallel veins throughout.  Eudicots, which include brassicas, legumes, and most common garden shrubs and trees, have leaves that are held away from the stem by stalks, termed petioles, and typically have broad laminas with net-like veins.

In grasses, the base of the leaf forms a tube-like structure, called the sheath. The sheath allows the plant to increase in height while keeping its growing tip close to the ground, protecting it from the blades of lawnmowers or incisors of herbivores.

In the 19^th Century, botanists proposed that the grass sheath was equivalent to the petiole of eudicot leaves. But this view was challenged in the 20^th century when plant anatomists noted that petioles have parallel veins, similar to the grass leaf, and concluded that the entire grass leaf (except for a tiny region at its tip) was derived from the petiole. 

Using recent advances in computational modeling and developmental genetics, the team revisited the problem of grass development.  They modeled different hypotheses for how grass leaves grow and tested the predictions of each model against experimental results.  To their surprise, they found that the model based on the 19th-century idea of sheath-petiole equivalence was much more strongly supported than the current view.

This mirrors findings in animal development where a discarded theory - that the ‘underbelly’ side of insects corresponds to the back of vertebrates like us – was vindicated in the light of fresh developmental genetic research.

The grass study shows how simple modulations of growth rules, based on a common pattern of gene activities, can generate a remarkable diversity of different leaf shapes, without which our gardens and dining tables would be much poorer.

Evolution of the grass leaf by primordium extension and petiole-lamina remodelling, is published in the journal Science. 10.1126/science.abf9407

The Artificial Intelligence industry should create a global community of hackers and “threat modelers” dedicated to stress-testing the harmful potential of new AI products to earn the trust of governments and the public before it’s too late.

This is one of the recommendations made by an international team of risk and machine-learning experts, led by researchers at the University of Cambridge’s Centre for the Study of Existential Risk (CSER) in the UK, who has authored a new “call to action” published today in the journal Science.

They say that companies building intelligent technologies should harness techniques such as “red team” hacking, audit trails, and “bias bounties” – paying out rewards for revealing ethical flaws – to prove their integrity before releasing AI for use on the wider public.    

Otherwise, the industry faces a “crisis of trust” in the systems that increasingly underpin our society, as public concern continues to mount over everything from driverless cars and autonomous drones to secret social media algorithms that spread misinformation and provoke political turmoil.

The novelty and “black box” nature of AI systems, and ferocious competition in the race to the marketplace, have hindered the development and adoption of auditing or third-party analysis, according to lead author Dr. Shahar Avin of CSER. 

The experts argue that incentives to increase trustworthiness should not be limited to regulation, but must also come from within an industry yet to fully comprehend that public trust is vital for its future – and trust is fraying.     

The new publication puts forward a series of “concrete” measures that they say should be adopted by AI developers.

“There are critical gaps in the processes required to create AI that has earned public trust. Some of these gaps have enabled questionable behavior that is now tarnishing the entire field,” said Avin.

“We are starting to see a public backlash against technology. This ‘tech-lash’ can be all-encompassing: either all AI is good or all AI is bad.

“Governments and the public need to be able to easily tell apart between the trustworthy, the snake-oil salesmen, and the clueless,” Avin said. “Once you can do that, there is a real incentive to be trustworthy. But while you can’t tell them apart, there is a lot of pressure to cut corners.”

Co-author and CSER researcher Haydn Belfield said: “Most AI developers want to act responsibly and safely, but it’s been unclear what concrete steps they can take until now. Our report fills in some of these gaps.”

The idea of AI “red teaming” – sometimes known as white-hat hacking – takes its cue from cyber-security.

“Red teams are ethical hackers playing the role of malign external agents,” said Avin. “They would be called in to attack any new AI, or strategize on how to use it for malicious purposes, in order to reveal any weaknesses or potential for harm.”

While a few big companies have the internal capacity to “red team” – which comes with its ethical conflicts – the report calls for a third-party community, one that can independently interrogate new AI and share any findings for the benefit of all developers.

A global resource could also offer high-quality red teaming to the small start-up companies and research labs developing AI that could become ubiquitous.  

The new report, a concise update of more detailed recommendations published by a group of 59 experts last year, also highlights the potential for bias and safety “bounties” to increase openness and public trust in AI.

This means financially rewarding any researcher who uncovers flaws in AI that have the potential to compromise public trust or safety – such as racial or socioeconomic biases in algorithms used for medical or recruitment purposes.

Earlier this year, Twitter began offering bounties to those who could identify biases in their image-cropping algorithm.

Companies would benefit from these discoveries, say researchers, and be given time to address them before they are publicly revealed. Avin points out that, currently, much of this “pushing and prodding” is done on a limited, ad-hoc basis by academics and investigative journalists.

The report also calls for auditing by trusted external agencies – and for open standards on how to document AI to make such auditing possible – along with platforms dedicated to sharing “incidents”: cases of undesired AI behavior that could cause harm to humans.

These, along with meaningful consequences for failing an external audit, would significantly contribute to an “ecosystem of trust” say the researchers.

“Some may question whether our recommendations conflict with commercial interests, but other safety-critical industries, such as the automotive or pharmaceutical industry, manage it perfectly well,” said Belfield.

“Lives and livelihoods are ever more reliant on AI that is closed to scrutiny, and that is a recipe for a crisis of trust. It’s time for the industry to move beyond well-meaning ethical principles and implement real-world mechanisms to address this,” he said.

Added Avin: "We are grateful to our collaborators who have highlighted a range of initiatives aimed at tackling these challenges, but we need policy and public support to create an ecosystem of trust for AI.”